Artificial Intelligence

(Prof. Stuckenschmidt)

Applications for Bachelor and Master Thesis

The Chair of Artificial Intelligence (Prof. Stuckenschmidt) offers the topics for master thesis that can be found here. Applications should be send to the contact mentioned on that page. For bachelor thesis we do not offer such a list. Please directly contact the lecturer/researcher of the course/topic you are interested in.

Artificial Intelligence Research Group

We conduct fundamental and applied research in Artificial Intelligence. We develop AI methods that address the specific challenges of a number of application areas in Industry and Society:

Supply Chain Management, Process Management, Smart Mobility, Activity Tracking, Educational Data Mining, Rule Learning, Tree-based Learning, Reinforcement Learning, Time Series Methods, Causal Modelling, Simulation, Graph Neural Networks,

News

2 Papers Accepted for BPM 2026

We are happy to announce that the following papers have been accepted for BPM 2026 in Toronto, Canada Lukas Kirchdorfer, Artemis Doumeni, Han van der Aa and Hugo A. López: From Global Policies to Local Strategies: Multi-Objective Optimization of Resource-Specific Handover PoliciesKeyvan Amiri ...

Paper on Multi-Task Learning Accepted for the International Journal of Computer Vision

We are happy to announce that the paper “Investigating Uncertainty Weighting for Multi-Task Learning: Insights and Analytical Alternatives” by Lukas Kirchdorfer, Tobias Sesterhenn, Christian Bartelt, Heiner Stuckenschmidt, Lukas Schott and Jan M. Köhlerhas been accepted for the International Journal ...

Paper on Destination Prediction for Micromobility accepted

We are happy to announce that Daniel Kerger has his first paper “Multi-Operator Free-Floating GBFS Trip Destination Prediction in Public Mobility Sharing Systems” accepted at the Journal of Cycling and Micromobility Research (JCMR) published by Elsevier.

Paper Accepted in the European Journal of Operational Research

We are happy to announce that the paper “Data-driven Inventory Management under Customer Substitution” by Sebastian Müller, Jakob Huber, Ralph Alexander Bubak, Moritz Fleischmann and Heiner Stuckenschmidt has been accepted for the European Journal of Operational Research. The paper is a follow-up of ...

all news

People

Faculty
Prof. Dr. Heiner Stuckenschmidt Artificial Intelligence
Dr. Christian Meilicke: Knowledge Representation and Reasoning
Researchers
Keyvan Amiri Elyasi: Predictive Process Mining with Deep Neural Networks
Lea Cohausz: Causal Models and Fair ML in Educational Data Mining
Thilo Dieing: Social Data Science
Julia Gastinger: Temporal Graph Mining
Daniel Kerger: Enhancing and understanding public sharing networks
Ricarda Link: Motion-based Human Activity Recognition
Konrad Özdemir: Temporal Machine Learning
Darshit Pandya: Activity Monitoring using Automatic Speech Recognition (ASR) systems
PhD Students
Julian Aßmann (SAP): Downsizing Large Language Models
Alexander Bubak: Data-Driven Inventory Management
Christoph Huber (HS Mannheim): Visualization of Smart City Data
Jakob Kappenberger: Social Simulation and Algorithmic Decision Making
Mareike Keil (em: AG) UI-Design for Neurodivergent Users
Lukas Kirchdorfer (SAP Signavio): Data-driven Business Process Simulation
Andreas Meyer-Lindenberg (ZI): Digital Mind Twins
Sebastian Paull (AIPERIA): Machine Learning for Master Production Planning
Andrej Tschalzev (InES): Deep Learning for Tabular Data
Nils Wilken (InES): Symbolic Goal Recognition
Completed PhDs
Prof. Dr. Erman Acar (2018): “Knowledge Representation for Automated Decision Making”
Dr. Taha Alhersh (2021): “From Motion to Human Activity Recognition”
Dr. Sarah Alturki (2022): “Predicting Student Performance in Interdisciplinary Programs using Methods of Educational Data Mining”.
Dr. Elena Beisswanger (2013): “Developing Ontological Background Knowledge for Biomedicine”.
Dr. Patrick Betz: (2025): “Symbolic Rule-based Knowledge Graph Completion”
Dr. Fabian Burzlaff (2021): “Knowledge-Driven Architecture Composition”.
Dr. Alexander Diete (2021): “Towards Multimodal Activity Recognition in Complex Scenarios”
Dr. Arnab Dutta (2016): “Automated Knowledge Base Extension Using Open Information”.
Prof. Dr. Kai Eckert (2012): “Usage-driven maintenance of knowledge organization systems”.
Dr. Daniel Fleischhacker (2016): “Detecting Errors in Linked Data Using Ontology Learning and Outlier Detection”.
Dr. Oliver Frendo (2021): “Improving Smart Charging for Electric Vehicle Fleets by Integrating Battery and Prediction Models”.
Dr. David Friede (2023): “Exploring discrete representations in stochastic computation graphs Challenges, benefits, and novel strategies”.
Dr. Rim Helaoui* (2016): “On Leveraging Statistical and Relational Information for the Representation and Recognition of Complex Human Activities”.
Dr. Jakob Huber* (2019): “Data-driven Decision Support for Perishable Goods”.
Dr. Jonathan Kobbe (2023): “Automatic generation of structured explanations for arguments from consequences”.
Dr. Elena Kuss (2019): “Evaluation of Process Model Matching Techniques”.
Dr. Sascha Marton* (2025): “Learning Tree-based Models with Gradient Descent”
Dr. Christian Meilicke* (2011): “Alignment Incoherence in Ontology Matching”.
Dr. Jan Noessner* (2014): “Efficient Maximum A-Posteriori Inference in Markov Logic and Application in Description Logics”.
Dr. Andreas Nolle (2021): “Federated Knowledge Base Debugging in DL-Lite_A".
Dr. Michael Oesterle (2024) “Self-learning restriction-based governance of multi-agent systems”.
Dr. Simon Ott (2026): “Rehabilitating Rule-Based Methods for Knowledge Graph Reasoning: From Aggregation to Application”.
Dr. Christoph Pinkel (2016): “Incremental, Interactive,Inter-Model Mapping Generation”.
Dr. Bernhard Schäfer* (2023): “Recognizing Hand-drawn Diagrams in Images”
Dr. Anne Schlicht* (2012): “Scaling Up Description Logic Reasoning by Distributed Resolution”.
Dr. Jörg Schönfisch (2018): “Scalable Handling of Uncertain Data and Knowledge Graphs”
Dr. Diana Sola (2023): “Recommending activities for business process model”.
Dr. Christian Schreckenberger (2026): “Online Learning in Open Feature Spaces”.
Dr. Timo Sztyler* (2019): “Sensor-based human activity recognition: Overcoming issues in a real world setting”
Dr. Christoph Theil (2022): “Uncertainty, Risk, and Financial Disclosures -
Applications of Natural Language Processing in Behavioral Economics”.
Dr. Caecilia Zirn (2016): “Fine-grained Position Analysis for Political Texts”.
* With Distinction
Former Members still active in Science
Erman Acar – Assistant Professor in Explainable AI for Finance – University of Amsterdam
Sarah Alturki – Assistant Professor Open Arab University, Saudi Arabia
Christian Bartelt – Professor for Methods and Applications of Machine Learning at TU Clasuthal.
Melisachew Wudage Chekol – Assistant Professor for Data Management at the Utrecht University
Kai Eckert – Professor at University of Applied Science Mannheim
Ioana Hulpus – Assistant Professor at Utrecht University
Elena Kuss – Professor for Business Informatics Reuthlingen University of Applied Science
Goran Glavas – Professor for Natural Language Processing at Würzburg University
Stefan Lüdtke – Tenure Track Assistant Professor for Marine Data Science at University of Rostock
Federico Nanni - Research Data Scientist at the Allan Turing Institute London
Mathias Niepert – Professor for Machine Learning and Simulation at Stuttgart University
Diana Sola – Professor for Data Science and AI at DHBW Heidenheim
Timo Sztyler – Research Scientist at NEC Labs Europe Heidelberg

Projects

Projects

KISTe-BW: KI-gestützter Studieninteressenstest des Landes Baden-Württemberg (2025 – 2027)
NEST-bw: Netzwerk zu Verfahren der Studienorientierung und Selbstreflexion (2024 – )
Meeting KI: Entwicklung eines intelligenten Meeting- Unterstützungsystems (2024 – 2026)
TransforMA: Technologie- und Wissenstransfer für die aktive Gestaltung von Transformationsprozessen (2023 – 2027)
sMArt roots – Smart City Modellstadt Mannheim (2021 – 2026)

Software and Data

Activity Recognition Data and Algorithms
AnyBURL (a state of the art rule learner for Knowledge-Base Completion)
ALCOMO (a tool for repairing ontology alignments)
Rockit (a query engine for Markov Logic)
ELOG (a reasoner for log-linear description logics.
PyClause (a python library to support rule learning and rule application)

2026

Kerger, D. and Stuckenschmidt, H. (2026). Multi-operator free-floating GBFS trip destination prediction in public mobility sharing systems. Journal of Cycling and Micromobility Research, 7, 1–14.
Kirchdorfer, L., Sesterhenn, T., Bartelt, C., Stuckenschmidt, H., Schott, L. and Köhler, J. M. (2026). Investigating uncertainty weighting for multi-task learning: Insights and analytical alternative. International Journal of Computer Vision, 134, 1–19.
Kirchdorfer, L., Özdemir, K., der Aa, H. and Stuckenschmidt, H. (2026). Arrival times in dynamic environments: modeling, evaluation, and benchmarking for business process simulation. Process Science, 3, 1–29.
Kraus, A., Amiri Elyasi, K., Rebmann, A., Hadian, S. and der Aa, H. (2026). A framework for steady-state detection in process mining. Information Systems : IS, 140, 1–20.
Schilpp, J., Sabherwal, R., Wepper, M.-R. and Höhle, H. (2026). Does knowledge proximity attenuate or amplify the impact of credibility signals on knowledge use? Insights from two natural experiments. Information & Management, 63, 1–20.

2025

Amiri Elyasi, K., Sola, D., Meilicke, C., der Aa, H. and Stuckenschmidt, H. (2025). Knowledge graph completion for activity recommendation in business process modeling. Künstliche Intelligenz : KI, 39, 181–195.
Bubak, A., Rolf, B., Reggelin, T., Lang, S. and Stuckenschmidt, H. (2025). An LSTM network-based genetic algorithm for integrated procurement and scheduling optimisation. International Journal of Production Research, 63, 4036-4065.
Cohausz, L., Kappenberger, J. and Stuckenschmidt, H. (2025). Causal graphs and fairness in machine learning: addressing practical challenges in causal fairness evaluation. Journal of Artificial Intelligence Research : JAIR, 84, 1–34.
Kappenberger, J. and Stuckenschmidt, H. (2025). Overcoming data scarcity in calibrating SUMO scenarios with evolutionary algorithms. SUMO Conference Procedings, 6, 133–148.
Kappenberger, J., Stuckenschmidt, H. and Gerdon, F. (2025). Pricing parking for fairness — A simulation study based on an empirically calibrated model of parking behavior. Transportation Research. Part A, Policy and Practice, 193, 1–29.
Kirchdorfer, L., Blümel, R., Kampik, T., der Aa, H. and Stuckenschmidt, H. (2025). Discovering multi-agent systems for resource-centric business process simulation. Process Science, 2, 1–26.
Paull, S., Bubak, A. and Stuckenschmidt, H. (2025). Machine learning for master production scheduling: combining probabilistic forecasting with stochastic optimisation. Expert Systems with Applications, 271, 1–13.
Rink, J., Szabo, K., Hoyer, C., Saver, J. L., Nour, M., Audebert, H. J., Kunz, W. G., Froelich, M. F., Heinzl, A., Tschalzev, A., Hoffmann, J., Schoenberg, S. O. and Tollens, F. (2025). Mobile stroke units services in Germany: A cost-effectiveness modeling perspective on catchment zones, operating modes, and staffing. European Journal of Neurology, 32, 1–9.
Rolf, B., Beier, A., Jackson, I., Müller, M., Reggelin, T., Stuckenschmidt, H. and Lang, S. (2025). A review on unsupervised learning algorithms and applications in supply chain management. International Journal of Production Research, 63, 1933-1983.

2024

Cohausz, L., Tschalzev, A., Bartelt, C. and Stuckenschmidt, H. (2024). Investigating demographic features and their connection to performance, predictions, and fairness in EDM models. Journal of Educational Data Mining, 16, 177–213.
Marton, S., Lüdtke, S., Bartelt, C., Tschalzev, A. and Stuckenschmidt, H. (2024). Explaining neural networks without access to training data. Machine Learning, 113, 3633-3652.
Meilicke, C., Chekol, M. W., Betz, P., Fink, M. and Stuckenschmidt, H. (2024). Anytime bottom-up rule learning for large-scale knowledge graph completion. The VLDB Journal, 33, 131–161.
Rink, J., Tollens, F., Tschalzev, A., Bartelt, C., Heinzl, A., Hoffmann, J., Schoenberg, S. O., Marzina, A., Sandikci, V., Wiegand, C., Hoyer, C. and Szabo, K. (2024). Establishing an MSU service in a medium-sized German urban area — clinical and economic considerations. Frontiers in Neurology, 15, 1–9.
Spreitzenbarth, J., Bode, C. and Stuckenschmidt, H. (2024). Artificial intelligence and machine learning in purchasing and supply management: A mixed-methods review of the state-of-the-art in literature and practice. Journal of Purchasing and Supply Management, 30, 1–21.

2023

Amaefule, C. O., Lüdtke, S., Klostermann, A., Hinz, C. A., Kampa, I., Kirste, T. and Teipel, S. (2023). At crossroads in a virtual city: Effect of spatial disorientation on gait variability and psychophysiological response among healthy older adults. Gerontology, 69, 450–463.
Kolthoff, K., Bartelt, C. and Ponzetto, S. P. (2023). Correction to: Data-driven prototyping via natural- language-based GUI retrieval. Automated Software Engineering, 30, 1–2.
Kolthoff, K., Bartelt, C. and Ponzetto, S. P. (2023). Data-driven prototyping via natural-language-based GUI retrieval. Automated Software Engineering, 30, 1–34.
Schäfer, B., der Aa, H., Leopold, H. and Stuckenschmidt, H. (2023). Sketch2Process: End-to-end BPMN Sketch Recognition Based on Neural Networks. IEEE Transactions on Software Engineering, 49, 2621-2641.
Spreitzenbarth, J., Bode, C. and Stuckenschmidt, H. (2023). Designing an AI purchasing requisition bundling generator. Computers in Industry, 155, Article 104043, 1–14.
Umlauft, J., Johnson, C. W., Roux, P., Trugman, D. T., Lecointre, A., Walpersdorf, A., Nanni, U., Gimbert, F., Rouet-Leduc, B., Hulbert, C., Lüdtke, S., Marton, S. and Johnson, P. A. (2023). Mapping glacier basal sliding applying machine learning. Journal of geophysical research : JGR. F, Earth surface, 128, 1–20.

2022

Alturki, S., Cohausz, L. and Stuckenschmidt, H. (2022). Predicting master’s students’ academic performance: An empirical study in Germany. Smart Learning Environments, 9, 1–22.
Alturki, S., Hulpus, I. and Stuckenschmidt, H. (2022). Predicting academic outcomes: A survey from 2007 till 2018. Technology, Knowledge and Learning, 27, 275–307.
Alturki, S. and Stuckenschmidt, H. (2022). Assessing students' self-assessment ability in an interdisciplinary domain. Journal of Applied Research in Higher Education : JARHE, 14, 1449-1466.
Burzlaff, F., Wilken, N., Bartelt, C. and Stuckenschmidt, H. (2022). Semantic interoperability methods for smart service systems: A survey. IEEE Transactions on Engineering Management : EM, 69, 4052-4066.
Cohausz, L. (2022). When probabilities are not enough – A framework for causal explanations of student success models. Journal of Educational Data Mining, 14, 52–75.
Marton, S., Lüdtke, S. and Bartelt, C. (2022). Explanations for neural networks by neural networks. Applied Sciences, 12, 1–14.
Niemann, F., Lüdtke, S., Bartelt, C. and Ten Hompel, M. (2022). Context-aware human activity recognition in industrial processes. Sensors, 22, 1–14.
Sola, D., der Aa, H., Meilicke, C. and Stuckenschmidt, H. (2022). Exploiting Label Semantics For Rule-Based Activity Recommendation In Business Process Modeling. Information Systems : IS, 108.
Teipel, S., Amaefule, C. O., Lüdtke, S., Görß, D., Faraza, S., Bruhn, S. and Kirste, T. (2022). Prediction of disorientation by accelerometric and gait features in young and older adults navigating in a virtually enriched environment. Frontiers in Psychology, 13, 1–14.

2021

Alhersh, T., Stuckenschmidt, H., Rehman, A. U. and Brahim Belhaouari, S. (2021). Learning human activity from visual data using deep learning. IEEE Access, 9, 106245-106253.
Alturki, S. and Alturki, N. (2021). Using educational data mining to predict students' academic performance for applying early interventions. Journal of Information Technology Education : JITE. Innovations in Practice : IIP, 20, 121–137.
Civitarese, G., Sztyler, T., Riboni, D., Bettini, C. and Stuckenschmidt, H. (2021). POLARIS: Probabilistic and ontological activity recognition in smart-homes. IEEE Transactions on Knowledge and Data Engineering : TKDE, 33, 209–223.
Frendo, O., Gaertner, N. and Stuckenschmidt, H. (2021). Open source algorithm for smart charging of electric vehicle fleets. IEEE Transactions on Industrial Informatics, 17, 6014-6022.
Frendo, O., Gärtner, N. and Stuckenschmidt, H. (2021). Improving smart charging prioritization by predicting electric vehicle departure time. IEEE Transactions on Intelligent Transportation Systems, 22, 6646-6653.
Huber, J. and Stuckenschmidt, H. (2021). Intraday shelf replenishment decision support for perishable goods. International Journal of Production Economics, 231, 1–14.
Schäfer, B., Keuper, M. and Stuckenschmidt, H. (2021). Arrow R-CNN for handwritten diagram recognition. International Journal on Document Analysis and Recognition : IJDAR, 24, 3–17.

2027

Amiri Elyasi, K., Tschalzev, A., der Aa, H. and Stuckenschmidt, H. (2027). QTab: Inter-case-aware remaining time prediction via queuing networks and tabular machine learning. In , Enterprise design, operations, and computing : 30th International Conference, EDOC 2026, Enschede, The Netherlands, September 15–18, 2026, Proceedings (S. ). Lecture Notes in Computer Science, Springer: Berlin [u. a.].

2026

Bleisinger, O., Keil, M. V. and Eigner, M. (2026). Comparison of evolutionary, reinforcement and active learning for simulation-based design space exploration. In , (S. 2203-2212). Proceedings of the Design Society, Cambridge University Press: Cambridge.
Kirchdorfer, L., Özdemir, K., Kusenic, S., der Aa, H. and Stuckenschmidt, H. (2026). A divide-and-conquer approach for modeling arrival times in business process simulation. In , Business Process Management : 23rd International Conference, BPM 2025, Seville, Spain, August 31 – September 5, 2025, proceedings (S. 325–342). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Özdemir, K., Kirchdorfer, L., Amiri Elyasi, K., der Aa, H. and Stuckenschmidt, H. (2026). Rethinking business process simulation: A utility-based evaluation framework. In , Business Process Management Forum : BPM 2025 Forum, Seville, Spain, August 31 – September 5, 2025, Proceedings (S. ). Lecture Notes in Business Information Processing : LNBIP, Springer: Berlin [u. a.].

2025

Amiri Elyasi, K., der Aa, H. and Stuckenschmidt, H. (2025). A simple and calibrated approach for uncertainty-aware remaining time prediction. In , Business Process Management : 23rd International Conference, BPM 2025, Seville, Spain, August 31 – September 5, 2025 (S. 217–234). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Cohausz, L. (2025). Why the future of AIED is causal: Arguments for creating a tradition based on causal thinking. In , Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, Blue Sky, and WideAIED; 26th International Conference, AIED 2025, Palermo, Italy, July 22 (S. 17–31). Communications in Computer and Information Science, Springer: Berlin [u. a.].
Cohausz, L., Dieing, T. I., Merkle, B., Scheffler, M. and Janke, S. (2025). Smart start: A critical analysis of chatbots as tools to support study orientation. In , 23. Fachtagung Bildungstechnologien (DELFI 2025) : 8.-11. September 2025, Freiberg (S. 113–127). GI-Edition : Lecture Notes in Informatics. Proceedings, Gesellschaft für Informatik (GI): Bonn.
Dieing, T. I. and Cohausz, L. (2025). Where’s the f*** ing filter? A survey on handling problematic and sensitive questions in open educational chatbots. In , Artificial Intelligence in Education : 26th International Conference, AIED 2025, Palermo, Italy, July 22–26, 2025, proceedings, Part V (S. 108–115). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Huber, C., Nagel, T. and Stuckenschmidt, H. (2025). Comparing public dashboards in the wild : An observational study of urban data visualizations in an outdoor exhibition. In , 2025 29th International Conference Information Visualisation : IV 2025, 5–8 August 2025, Darmstadt, Germany, proceedings (S. 62–67). , IEEE: Piscataway, NJ.
Kappenberger, J., Strasser Ceballos, C., Gerdon, F., Szafran, D., Rupp, F., Eckert, K., Stuckenschmidt, H., Bach, R. L., Kreuter, F. and Kern, C. (2025). Unintended impacts of automation for integration? Simulating integration outcomes of algorithm-based refugee allocation in Germany. In , Proceedings of the Eighth AAAI/ACM Conference on AI, Ethics, and Society (AIES-25). Volume 2 (S. 1375-1387). , Association for the Advancement of ArtificialIntelligence: Washington, DC.
Link, R. H. and Stuckenschmidt, H. (2025). Mitigating null-class dominance in multiclass inertial-based activity recognition: HASCA-WEAR challenge – a technical report of team HARMA. In , UbiComp Companion '25 : Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing (S. 1012-1017). , Association for Computing Machinery: New York, NY.
Marton, S., Grams, T., Vogt, F., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2025). Mitigating information loss in tree-based reinforcement learning via direct optimization. In , The Thirteens International Conference on Learning Representations (S. ). , OpenReview.net: .
Marton, S., Schneider, M., Brinkmann, J., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2025). Decision trees that remember: Gradient-based learning of recurrent decision trees with memory. In , The Thirteens International Conference on Learning Representations (S. ). , OpenReview.net: .
Nick, E., Purucker, L., Tschalzev, A., Holzmüller, D., Desai, P. M., Salinas, D. and Hutter, F. (2025). TabArena: A living benchmark for machine learning on tabular data. In , Advances in Neural Information Processing Systems : NeurIPS 2025 (S. 1–66). , NeurIPS: Vancouver, BC.
Ott, S., Chekol, M. W., Meilicke, C. and Stuckenschmidt, H. (2025). Training-free score calibration for complex query decomposition. In , The Semantic Web : 22nd European Semantic Web Conference, ESWC 2025, Portoroz, Slovenia, June 1–5, 2025. Proceedings, Part I (S. 188–207). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Pandya, D. and Stuckenschmidt, H. (2025). Explainable acoustic scene classification with audio tags and feature ablation. In , 25th DSP 2025 : 2025 25th International Conference on Digital Signal Processing (DSP 2025) : date of conference: 25–27 June 2025, conference location: Pylos (Messinia, Southwest Peloponnese), Greece (S. 1–5). , IEEE: Piscataway, NJ.

2024

Amiri Elyasi, K., der Aa, H. and Stuckenschmidt, H. (2024). PGTNet: a process graph transformer network for remaining time prediction of business process instances. In , Advanced information systems egineering : 36th international conference, CAiSE 2024, Limassol, Cyprus, June 3–7, 2024 : proceedings (S. 124–140). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Betz, P., Galarraga, L., Ott, S., Meilicke, C., Suchanek, F. M. and Stuckenschmidt, H. (2024). PyClause – Simple and efficient rule handling for knowledge graphs. In , Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence : Demo Track, Jeju, Korea, 3–9 August 2024 (S. 8610-8613). , International Joint Conferences on Artificial Intelligence: Vienna.
Betz, P., Lüdtke, S., Meilicke, C. and Stuckenschmidt, H. (2024). Rule confidence aggregation for knowledge graph completion. In , Rules and reasoning : 8th International Joint Conferrence, RuleML+RR 2024, Bucharest, Romania, September 16–18, 2024, proceedings (S. 32–49). Lecture Notes in Computer Science, Springer: Cham.
Cohausz, L., Kappenberger, J. and Stuckenschmidt, H. (2024). Combining fairness and causal graphs to advance both. In , Fairness and Bias in AI : Proceedings of the 2nd Workshop on Fairness and Bias in AI, co-located with 27th European Conference on Artificial Intelligence (ECAI 2024) (S. 1–14). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Cohausz, L., Kappenberger, J. and Stuckenschmidt, H. (2024). What fairness metrics can really tell you: A case study in the educational domain. In , LAK'24: Proceedings of the 14th Learning Analytics and Knowledge Conference (S. 792–799). , Association for Computing Machinery: Kyoto, Japan.
Dieing, T. I., Scheffler, M. and Cohausz, L. (2024). Enhancing chatbot-assisted study program orientation. In , Workshopband der 22. Fachtagung Bildungstechnologien (DELFI) : 09.09.-11.09.2024, Fulda, Deutschland (S. 223–232). , Gesellschaft für Informatik (GI): Bonn.
Gastinger, J., Huang, S., Galkin, M., Loghmani, E., Parviz, A., Poursafaei, F., Danovitch, J., Rossi, E., Koutis, I., Stuckenschmidt, H., Rabbany, R. and Rabusseau, G. (2024). TGB 2.0: A benchmark for learning on temporal knowledge graphs and heterogeneous graphs. In , Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track (S. 140199-140229). , Curran Associates, Inc.: Vancouver, BC.
Gastinger, J., Meilicke, C., Errica, F., Sztyler, T., Schuelke, A. and Stuckenschmidt, H. (2024). History repeats itself: A baseline for temporal knowledge graph forecasting. In , Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence : Jeju, 3–9 August 2024 (S. 4016-4024). , International Joint Conferences on Artificial Intelligence: Vienna.
Karvonen, A., Wright, B., Rager, C., Angell, R., Brinkmann, J., Smith, L. R., Verdun, C. M., Bau, D. and Marks, S. (2024). Measuring progress in dictionary learning for language model interpretability with board game models. In , ICML 2024 Workshop on Mechanistic Interpretability (S. 1–17). , OpenReview: .
Keil, M. (2024). AI-supported UI design for enhanced development of neurodiverse-friendly IT-systems. In , Proceedings of NordDesign 2024 : Reykjavik, Iceland, 12th-14th August 2024 (S. 392–401). , The Design Society: .
Kirchdorfer, L., Blümel, R., Kampik, T., der Aa, H. and Stuckenschmidt, H. (2024). AgentSimulator: an agent-based approach for data-driven business process simulation. In , 2024 6th International Conference on Process Mining : ICPM 2024 : Kgs. Lyngby, Denmark, 14–18 October 2024 : proceedings (S. 97–104). , IEEE: Piscataway, NJ.
Klerings, A., Bartelt, C. and Mueller, A. (2024). Developmentally plausible multimodal language models are highly modular. In , The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning : proceedings of the Second BabyLM Challenge : November 15–16, 2024 (S. 118–139). , Association for Computational Linguistics: Miami, FL, USA.
Kolthoff, K., Bartelt, C., Ponzetto, S. P. and Schneider, K. (2024). Self-elicitation of requirements with automated GUI prototyping. In , Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering : ASE 2024 : October 28 – November 1, 2024, Sacramento, California, USA (S. 2354-2357). , IEEE/ACM: Sacramento, CA, USA.
Kolthoff, K., Kretzer, F., Bartelt, C., Maedche, A. and Ponzetto, S. P. (2024). Interlinking user stories and GUI prototyping: A semi-automatic LLM-based approach. In , 32nd IEEE International Requirements Engineering Conference, 24–28 June 2024, Reykjavik, Iceland (S. 380–388). , IEEE: Piscataway, NJ.
Marton, S., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2024). GRANDE: Gradient-Based Decision Tree Ensembles for tabular data. In , International Conference on Learning Representations (S. 1–27). , OpenReview.net: .
Marton, S., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2024). GradTree: Learning axis-aligned decision trees with gradient descent. In , Proceedings of the 38th AAAI Conference on Artificial Intelligence (S. 14323-14331). , AAAI Press: Washington, DC.
Oesterle, M., Grams, T. and Bartelt, C. (2024). DRAMA at the PettingZoo: Dynamically restricted action spaces for multi-agent reinforcement learning frameworks. In , Proceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024, Hilton Hawaiian Village Waikiki Beach Resort, Hawaii, USA, January 3–6, 2024 (S. 7810-7819). , Department of IT-Management, Shidler College of Business, University of Hawaii: Honolulu, HI.
Oesterle, M., Grams, T., Bartelt, C. and Stuckenschmidt, H. (2024). RAISE the bar: Restriction of action spaces for improved social welfare and equity in traffic management. In , Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (S. 1492-1500). , International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC.
Ott, S., Meilicke, C. and Stuckenschmidt, H. (2024). Reevaluation of inductive link prediction. In , Rules and reasoning : 8th international joint conference, RuleML+RR 2024, Bucharest, Romania, September 16–18, 2024 : proceedings (S. 75–90). Lecture notes in computer science, Springer: Cham.
Pandya, D. and Stuckenschmidt, H. (2024). Evaluating the impact of different acoustic contexts on German speech recognition. In , 2024 IEEE international conference on pervasive computing and communications workshops and other affiliated events (PerCom workshops) : 11–15 March 2024 in Biarritz, France (S. 1–5). , IEEE: Piscataway, NJ.
Rana, A., Oesterle, M. and Brinkmann, J. (2024). GOV-REK: Governed Reward Engineering Kernels for designing robust multi-agent reinforcement learning systems. In , AAMAS '24: proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (S. 2429-2431). , International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC.
Scheffler, M., Dieing, T. I. and Cohausz, L. (2024). Developing a personalized study program recommender. In , Workshopband der 22. Fachtagung Bildungstechnologien (DELFI) : 9.-11. September 2024, Fulda, Deutschland (S. 233–240). , Gesellschaft für Informatik (GI): Bonn.
Tschalzev, A., Marton, S., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2024). A data-centric perspective on evaluating machine learning models for tabular data. In , Advances in Neural Information Processing Systems 37 (NeurIPS 2024) (S. 95896-95930). , Curran Associates Inc.: Red Hook, NY.
Tschalzev, A., Nitschke, P., Kirchdorfer, L., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2024). Enabling mixed effects neural networks for diverse, clustered data using Monte Carlo methods. In , Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence: Jeju, 03–09 August 2024 (S. ). , International Joint Conferences on Artificial Intelligence: Jeju, South Korea.
Wilken, N., Cohausz, L., Bartelt, C. and Stuckenschmidt, H. (2024). Fact Probability Vector Based Goal Recognition. In , 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024) (S. 4254-4261). Frontiers in Artificial Intelligence and Applications, IOS Press: Amsterdam [u. a.].

2023

Brinkmann, J., Swoboda, P. and Bartelt, C. (2023). A multidimensional analysis of social biases in vision transformers. In , 2023 IEEE/CVF International Conference on Computer Vision : ICCV 2023, Paris, France, 2–6 October 2023, proceedings (S. 4891-4900). , IEEE: Piscataway, NJ.
Busch, K., Rochlitzer, A., Sola, D. and Leopold, H. (2023). Just tell me: Prompt engineering in business process management. In , Enterprise, business-process and information systems modeling : 24th International Conference, BPMDS 2023, and 28th International Conference, EMMSAD 2023, Zaragoza, Spain, June 12–13, 2023, proceedings (S. 3–11). Lecture Notes in Business Information Processing : LNBIP, Springer: Berlin [u. a.].
Cohausz, L., Tschalzev, A., Bartelt, C. and Stuckenschmidt, H. (2023). Investigating the importance of demographic features for EDM-predictions. In , Proceedings of the 16th International Conference on Educational Data Mining (S. 125–136). , International Educational Data Mining Society: Bengaluru, India.
Ernst, J. S., Marton, S., Brinkmann, J., Vellasques, E., Foucard, D., Kraemer, M. and Lambert, M. (2023). Bias mitigation for large language models using adversarial learning. In , Proceedings of the 1st Workshop on Fairness and Bias in AI co-located with 26th European Conference on Artificial Intelligence (ECAI 2023),Kraków, Poland, October 1st, 2023 (S. 1–14). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Friede, D., Reimers, C., Stuckenschmidt, H. and Niepert, M. (2023). Learning disentangled discrete representations. In , Machine learning and knowledge discovery in databases : research track : European conference, EMCL PKDD 2023, Turin, Italy, september 18–22,2023, proceedings, part IV (S. 593–609). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Gastinger, J., Sztyler, T., Sharma, L., Schuelke, A. and Stuckenschmidt, H. (2023). Comparing apples and oranges? : on the evaluation of methods for temporal knowledge graph forecasting. In , Machine learning and knowledge discovery in databases: research track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023 : proceedings. Part III (S. 533–549). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Gautschi, T., Gschwend, T., Oberländer, L., Ponzetto, S. P. and Stuckenschmidt, H. (2023). Der Mannheim Master in Social Data Science. In , INFORMATIK 2023 : Designing Futures: Zukünfte gestalten (S. 81–88). GI-Edition : Lecture Notes in Informatics. Proceedings, Gesellschaft für Informatik (GI): Bonn.
He, Y., Schreckenberger, C., Stuckenschmidt, H. and Wu, X. (2023). Towards utilitarian online learning – A review of online algorithms in open feature space. In , Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (S. 6647-6655). , International Joint Conferences on Artificial Intelligence Organization: Macao SAR.
Keil, M. V. and Bleisinger, O. (2023). Benutzeroberflächen von MBSE-Tools und deren Auswirkung auf neurodivergente Systemarchitekten. In , Tag des Systems Engineering : Würzburg, 15.-17. November 2023 (S. 9–15). , GfSE-Verlag: Bremen.
Kobbe, J., Hulpus, I. and Stuckenschmidt, H. (2023). Effect graph: Effect relation extraction for explanation generation. In , Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE) (S. 116–127). , Association for Computational Linguistics: Toronto, Canada.
Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2023). Outlying aspect mining via sum-product networks. In , Advances in knowledge discovery and data mining: 27th Pacific-Asia Conference on knowledge discovery and data mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023 : proceedings. Part I (S. 27–38). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Marton, S., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2023). GradTree: Learning axis-aligned decision trees with gradient descent. In , (S. 1–17). , Neural Information Processing Systems Foundation, Inc. (NeurIPS): New Orleans.
Oesterle, M. and Sharon, G. (2023). Socially optimal non-discriminatory restrictions for continuous-action games. In , Proceedings of the 37th AAAI Conference of Artificial Intelligence. Vol. 10 (S. 11638-11646). , AAAI Press: Washington, DC.
Oesterle, M. and Sharon, G. (2023). Socially optimal non-discriminatory restrictions for continuous-action games. In , KI 2023: Advances in Artificial Intelligence : 46th German Conference on AI, Berlin, Germany, September 26–29, 2023, Proceedings (S. 252–256). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Ott, S., Betz, P., Stepanova, D., Gad-Elrab, M. H., Meilicke, C. and Stuckenschmidt, H. (2023). Rule-based knowledge graph completion with canonical models. In , CIKM'23: Proceedings of the 32nd ACM International Conference on Information & Knowledge Management (S. 1971-1981). , ACM: Birmingham, United Kingdom.
Popko, M., Bader, S., Lüdtke, S. and Kirste, T. (2023). Discovering behavioural predispositions in data to improve human activity recognition. In , iWOAR '22: Proceedings of the 7th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence : September 19–20, 2022, Rostock, Germany (S. 1–7). , Association for Computing Machinery: New York, NY, USA.
Schreckenberger, C., He, Y., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2023). Online random feature forests for learning in varying feature spaces. In , Proceedings of the 37th AAAI Conference on Artificial Intelligence. Vol. 4 (S. 4587-4595). , AAAI Press: Washington, DC.
Sola, D., der Aa, H., Meilicke, C. and Stuckenschmidt, H. (2023). Activity recommendation for business process modeling with pre-trained language models. In , The Semantic Web : 20th International Conference, ESWC 2023, Hersonissos, Crete, Greeece, May 28-June 1, 2023. Proceddings (S. 316–334). , Springer International Publishing: Cham, Switzerland.
Sola, D., Warmuth, C., Schäfer, B., Badakhshan, P., Rehse, J.-R. and Kampik, T. (2023). SAP Signavio Academic Models: A large process model dataset. In , Process Mining Workshops : ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23–28, 2022, Revised Selected Papers (S. 453–465). Lecture Notes in Business Information Processing : LNBIP, Springer: Berlin [u. a.].
Spreitzenbarth, J., Bode, C. and Stuckenschmidt, H. (2023). Designing an AI-enabled bundling generator in an automotive case study. In , Proceedings of the 56th Annual Hawaii International Conference on System Sciences (S. 4495-4504). , University of Hawaii at Manoa: Honolulu, HI.
Steinmann, L., Nowotka, D., Oberländer, L., Pfuhl, H., Stuckenschmidt, H. and Drechsler, R. (2023). Workshop: Aktuelle Entwicklungen und Perspektiven (an Hochschulen) im Bereich Data Science. In , Informatik 2023 : designing futures: Zukünfte gestalten ; 26. – 29. September 2023, Berlin (S. 73–80). GI-Edition : Lecture Notes in Informatics. Proceedings, Ges. für Informatik: Bonn.
Theil, K., Hovy, D. and Stuckenschmidt, H. (2023). Top-down influence? Predicting CEO personality and risk impact from speech transcripts. In , Proceedings of the seventeenth International AAAI Conference on web and social media : June 5th – 8th 2023, Limassol, Cyprus (S. 832–841). International AAAI Conference on Web and Social Media, AAAI Press: Palo Alto, Calif..
Wilken, N., Cohausz, L., Bartelt, C. and Stuckenschmidt, H. (2023). Planning landmark based goal recognition revisited: Does using initial state landmarks make sense? In , KI 2023: Advances in Artificial Intelligence : 46th Conference on AI, Berlin, Germany, September 26–29, 20023, proceedings (S. 231–244). Lecture Notes in Computer Science, Springer: Berlin [u. a.].

2022

Betz, P., Meilicke, C. and Stuckenschmidt, H. (2022). Adversarial explanations for knowledge graph embeddings. In , Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence : Vienna, 23–29 July 2022 (S. 2820-2826). , International Joint Conferences on Artificial Intelligence: Darmstadt ; Vienna.
Betz, P., Meilicke, C. and Stuckenschmidt, H. (2022). Supervised knowledge aggregation for knowledge graph completion. In , The semantic web: 19th International Conference, ESWC 2022, Hersonissos, Crete, Greece, May 29-June 2, 2022 : proceedings (S. 74–92). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Cohausz, L. (2022). Towards real interpretability of student success prediction combining methods of XAI and social science. In , Proceedings of the 15th International Conference on Educational Data Mining (S. 361–367). , International Educational Data Mining Society: Durham.
Cohausz, L., Wilken, N. and Stuckenschmidt, H. (2022). Plan-similarity based heuristics for goal recognition. In , 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) : PerCom Workshops 2022 (S. 316–321). , IEEE: Pisa.
Friede, D. and Niepert, M. (2022). Efficient learning of discrete-continuous computation graphs. In , 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6–14 December 2021 (S. 6720-6732). Advances in Neural Information Processing Systems, Curran Associates: Red Hook, NY.
Kappenberger, J., Theil, K. and Stuckenschmidt, H. (2022). Evaluating the impact of AI-based priced parking with social simulation. In , Social Informatics: 13th International Conference, SocInfo 2022, Glasgow, UK, October 19–21, 2022, proceedings (S. 54–75). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2022). Exchangeability-aware sum-product networks. In , Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vienna, 23–29 July 2022 (S. 4864-4870). , International Joint Conferences on Artificial Intelligence Organization: Wien.
Oesterle, M., Bartelt, C., Lüdtke, S. and Stuckenschmidt, H. (2022). Self-learning governance of black-box multi-agent systems. In , Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XV : International Workshop, COINE 2022, virtual event, May 9, 2022, revised selected papers (S. 73–91). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Schreckenberger, C., Bartelt, C. and Stuckenschmidt, H. (2022). Dynamic forest for learning from data streams with varying feature spaces. In , Cooperative information systems : 28th International Conference, CoopIS 2022, Bozen-Bolzano, Italy, October 4–7, 2022, Proceedings (S. 95–111). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Sola, D., Meilicke, C., der Aa, H. and Stuckenschmidt, H. (2022). On the Use of Knowledge Graph Completion Methods for Activity Recommendation in Business Process Modeling. In , Business Process Management Workshops : BPM 2021 international workshops, Rome, Italy, September 6–10, 2021, revised selected papers (S. 5–17). Lecture Notes in Business Information Processing : LNBIP, Springer: Berlin [u. a.].
Wilken, N., Cohausz, L., Schaum, J., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2022). Leveraging planning landmarks for hybrid online goal recognition. In , International Conference on Automated Planning and Scheduling ICAPS (2022) : June 13–17, 2022, virtual (S. ). , CEUR Workshop Proceedings: Aachen.

2021

Betz, P., Niepert, M., Minervini, P. and Stuckenschmidt, H. (2021). Backpropagating through Markov Logic Networks. In , Proceedings of 15th International Workshop on Neural-Symbolic Learning and Reasoning as part of the 1st International Joint Conference on Learning & Reasoning (IJCLR 2021) Virtual conference, October 25–27, 2021. (S. ). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Burzlaff, F. and Bartelt, C. (2021). Knowledge-driven architecture composition: Assisting the system integrator to reuse integration knowledge. In , ICWE 2021 : 21st International Conference on Web Engineering, Biarritz, France, May 18–21, 2021 ; proceedings (S. 305–319). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Hoffmann, L., Bartelt, C. and Stuckenschmidt, H. (2021). Knowledge injection via ML-based initialization of neural networks. In , Proceedings of the CIKM 2021 Workshops (CIKMW 2021) co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021) : Gold Coast, Queensland, Australia, November 1–5,2021 (S. 1–6). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Meilicke, C., Betz, P. and Stuckenschmidt, H. (2021). Why a naive way to combine symbolic and latent knowledge base completion works surprisingly well. In , 3rd Conference on Automated Knowledge Base Construction (S. 1–26). , OpenReview.net: Online.
Metzger, N., Hoffmann, L., Bartelt, C., Stuckenschmidt, H., Wommer, M. and Bescos del Castillo, M. B. (2021). Towards trace-graphs for data-driven test case mining in the domain of automated driving. In , Third IEEE International Conference on Artificial Intelligence Testing: AITest 2021 : proceedings : 23–26 August 2021, online event (S. 41–48). , IEEE: Piscataway, NJ.
Nolte, F., Wilken, N. and Bartelt, C. (2021). Rendezvous delivery: Utilizing autonomous electric vehicles to improve the efficiency of last mile parcel delivery in urban areas. In , 2021 IEEE PerCom Workshops : PerAwareCity 2021, 6th IEEE Workshop on Pervasive Context-Aware Smart Cities and Intelligent Transportation Systems, March 22–26, 2021 in Kassel, Germany (S. 148–153). , IEEE Computer Society: Piscataway, NJ.
Pernpeintner, M. (2021). Self-learning governance of competitive multi-agent systems. In , Organic Computing : Doctoral Dissertation Colloquium 2020 (S. 47–63). Intelligent Embedded Systems, Kassel University Press: Kassel.
Pernpeintner, M. (2021). Toward a self-learning governance loop for competitive multi-attribute MAS. In , AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems : Richland, SC, Virtual Event United Kingdom, May, 2021 (S. 1619-1621). , International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC.
Pernpeintner, M., Bartelt, C. and Stuckenschmidt, H. (2021). Governing black-box agents in competitive multi-agent systems. In , Multi-Agent Systems : 18th European Conference, EUMAS 2021, virtual event, June 28–29, 2021, revised selected papers (S. 19–36). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Schäfer, B., der Aa, H., Leopold, H. and Stuckenschmidt, H. (2021). Sketch2BPMN: Automatic recognition of hand-drawn BPMN models. In , Advanced Information Systems Engineering : 33rd International Conference, CAiSE 2021, Melbourne, VIC, Australia, June 28 – July 2, 2021, proceedings (S. 344–360). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Schäfer, B. and Stuckenschmidt, H. (2021). DiagramNet: hand-drawn diagram recognition using visual arrow-relation detection. In , Document Analysis and Recognition – ICDAR 2021 : 16th International Conference, Lausanne, Switzerland, September 5–10, 2021, Proceedings, Part I (S. 614–630). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Sola, D. (2021). Towards a rule-based recommendation approach for business process modeling. In , Service-oriented computing – ICSOC 2020 Workshops : AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Dubai, United Arab Emirates, December 14–17, 2020 : proceedings (S. 25–31). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Sola, D., Meilicke, C., der Aa, H. and Stuckenschmidt, H. (2021). A rule-based recommendation approach for business process modeling. In , Advanced information systems engineering : 33rd International Conference, CAiSE 2021, Melbourne, VIC, Australia, June 28 – July 2, 2021, proceedings (S. 328–343). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Wilken, N. and Stuckenschmidt, H. (2021). Combining symbolic and statistical knowledge for goal recognition in smart home environments. In , 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (S. 26–31). , IEEE Computer Society: Piscataway, NJ.
Wilken, N., Stuckenschmidt, H. and Bartelt, C. (2021). Combining symbolic and data-driven methods for goal recognition. In , 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (S. 428–429). , IEEE Computer Society: Piscataway, NJ.

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